{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "8ed5ecc3",
   "metadata": {},
   "source": [
    "# 基于对象的可视化操作\n",
    "\n",
    "相对于基于函数的可视化操作，Matplotlib提供了更灵活的方式进行可视化操作——基于对象的方法进行操作。\n",
    "\n",
    "事实上，对Matplotlib来说，每张图都是一个Figure对象，可以通过`plt.figure()`函数产生。Figure对象可以通过`.add_axes()`方法向图中添加Axes对象。Axes对象可以进行绘图操作，调用它的`.plot()`方法相当于调用`plt.plot()`函数。\n",
    "\n",
    "例如："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "467166f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "x = np.linspace(0, 5, 10)\n",
    "y = x ** 2\n",
    "\n",
    "fig = plt.figure()\n",
    "\n",
    "axes = fig.add_axes([0.1, 0.1, 0.8, 0.8])\n",
    "axes.plot(x, y, 'r')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "f70c4ba5",
   "metadata": {},
   "source": [
    "绘制多张子图时，可以直接使用`plt.subplots()`函数来同时得到Figure对象和子图对应Axes对象数组："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "04f4ad9e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "def f(t):\n",
    "    return np.exp(-t) * np.cos(2*np.pi*t)\n",
    "\n",
    "t1 = np.arange(0.0, 5.0, 0.1)\n",
    "t2 = np.arange(0.0, 5.0, 0.02)\n",
    "\n",
    "fig, axes = plt.subplots(2, 1)\n",
    "axes[0].plot(t1, f(t1), 'bo', t2, f(t2), 'k')\n",
    "axes[1].plot(t2, np.cos(2*np.pi*t2), 'r--')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6619b0a1",
   "metadata": {},
   "source": [
    "也可以调用两次.add_axes()方法："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9d614004",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "def f(t):\n",
    "    return np.exp(-t) * np.cos(2*np.pi*t)\n",
    "\n",
    "t1 = np.arange(0.0, 5.0, 0.1)\n",
    "t2 = np.arange(0.0, 5.0, 0.02)\n",
    "\n",
    "fig = plt.figure()\n",
    "\n",
    "# 参数含义：左下角横坐标，纵坐标，横轴长度，纵轴长度\n",
    "axes = fig.add_axes([0.1, 0.5, 0.8, 0.3]) \n",
    "\n",
    "axes.plot(t1, f(t1), 'bo', t2, f(t2), 'k')\n",
    "\n",
    "# 参数含义：左下角横坐标，纵坐标，横轴长度，纵轴长度\n",
    "axes = fig.add_axes([0.1, 0.1, 0.8, 0.3])\n",
    "axes.plot(t2, np.cos(2*np.pi*t2), 'r--')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "73a26715",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
